Development and Application of 3D Kinematic Methodologies for Biomechanical Modelling in Adaptive Sports and Rehabilitation

Biomechanical analysis is widely used to assess human movement sciences, specifically using three-dimensional motion capture modelling. There are unprecedented opportunities to increase quantitative knowledge of rehabilitation and recreation for disadvantaged population groups. Specifically, 3D models and movement profiles for human gait analysis were generated with emphasis on post-stroke patients, with direct model translation to analyze equivalent measurements while horseback riding in use of the alternative form of rehabilitation, equine assisted activities and therapies (EAAT) or hippotherapy (HPOT). Significant improvements in gait symmetry and velocity were found within an inpatient rehabilitation setting for patients following a stroke, and the developed movement profiles for patients have the potential to address patient recovery timelines. For population groups, such as those following a cerebral incident, alternative forms of rehabilitation like EAAT and HPOT are largely unexplored. Within these studies, relevant muscular activations were found between healthy human gait and horseback riding, supporting the belief that horseback riding can stimulate similar movements within the rider. Even more, there was a strong correlation between the horse’s pelvic rotations, and the responsive joint moments and rotations of the rider. These findings could have greater implications in choosing horses, depending on the desired physical outcome, for EAAT and HPOT purposes. Similar approaches were also used to address another biomechanically disadvantage population, adaptive sport athletes. Utilizing similar methodologies, a novel 3D wheelchair tennis athlete model was created to analyze match-simulation assessments. Significant findings were present in the energy expenditure between two drill assessments. Overall, the quantitative results, coupled with the qualitative assessment chapter, provide a robust assessment of the effects of 3D movement analysis on rehabilitation and adaptive activities

Wearable devices for remote vital signs monitoring in the outpatient setting: an overview of the field

Early detection of physiological deterioration has been shown to improve patient outcomes. Due to recent improvements in technology, comprehensive outpatient vital signs monitoring is now possible. This is the first review to collate information on all wearable devices on the market for outpatient physiological monitoring. A scoping review was undertaken. The monitors reviewed were limited to those that can function in the outpatient setting with minimal restrictions on the patient’s normal lifestyle, while measuring any or all of the vital signs: heart rate, ECG, oxygen saturation, respiration rate, blood pressure and temperature. A total of 270 papers were included in the review. Thirty wearable monitors were examined: 6 patches, 3 clothing-based monitors, 4 chest straps, 2 upper arm bands and 15 wristbands. The monitoring of vital signs in the outpatient setting is a developing field with differing levels of evidence for each monitor. The most common clinical application was heart rate monitoring. Blood pressure and oxygen saturation measurements were the least common applications. There is a need for clinical validation studies in the outpatient setting to prove the potential of many of the monitors identified. Research in this area is in its infancy. Future research should look at aggregating the results of validity and reliability and patient outcome studies for each monitor and between different devices. This would provide a more holistic overview of the potential for the clinical use of each device

The implementation path of intelligent rehabilitation under the background of healthy China construction

The improvement of rehabilitation service capacity is an important part of the construction of a healthy China, and intelligent technology is a powerful means of rehabilitation development. This paper reviews the background of a series of national policies for the construction of a healthy China, analyzes and summarizes the many shortcomings that currently restrict the improvement of rehabilitation service capabilities, and proposes the implementation path of intelligent rehabilitation. By expounding the service process of intelligent rehabilitation, and analyzing in detail the intelligent technical means suitable for integration from the four key links of real-time health monitoring, remote home intelligent rehabilitation intervention, health classification evaluation standard system and health intervention standard system, the general framework of implementation path of intelligent rehabilitation is built. Taking hypertension rehabilitation as an example, the article introduces the intelligent rehabilitation practice exploration and reference model in three aspects: The research and development of hypertension intelligent equipment, the clinical research of hypertension rehabilitation and the construction of hypertension rehabilitation database. Finally, combined with the concept of intelligent interconnection of all things, the definition of “rehabilitation Internet of things” is proposed, and the time is right for intelligent rehabilitation in the context of building a healthy China

Monitoring changes in physical activity data during strength training of people with myotonic dystrophy type 1

Myotonic dytrophy type 1 (DM1) is an incurable neuromuscular disease and muscle weakness is a prominent symptom. Research has shown that strength training can be an interesting solution to help with this symptom. Therefore an assistive technology aiming at supervising strength training at home for people with DM1 has been developed and tested in the home of 10 patients for 10 weeks. As many change point detection (CPD) techniques have been used for monitoring change in activity data in the past, no one applied these techniques to physical activities of people with DM1 disease. Hence, physical activity data have been collected during the 10-week experiment and state-of-the-art CPD algorithm has been used to analyze changes in physical activity during the strength training program at home. The results prove that many challenges need to be addressed in this context and could act as a guideline for future works

Personalized functional health and fall risk prediction using electronic health records and in-home sensor data

Research has shown the importance of Electronic Health Records (EHR) and in-home sensor data for continuous health tracking and health risk predictions. With the increased computational capabilities and advances in machine learning techniques, we have new opportunities to use multi-modal health big data to develop accurate health tracking models. This dissertation describes the development, evaluation, and testing of systems for predicting functional health and fall risks in community-dwelling older adults using health data and machine learning techniques. In an initial study, we focused on organizing and de-identifying EHR data for analysis using HIPAA regulations. The dataset contained nine years of structured and unstructured EHR data obtained from TigerPlace, a senior living facility at Columbia, MO. The de-identification of this data was done using custom automated algorithms. The de-identified EHR data was used in several studies described in this dissertation. We then developed personalized functional health tracking models using geriatric assessments in the EHR data. Studies show that higher levels of functional health in older adults lead to a higher quality of life and improves the ability to age-in-place. Even though several geriatric assessments capture several aspects of functional health, there is limited research in longitudinally tracking the personalized functional health of older adults using a combination of these assessments. In this study, data from 150 older adult residents were used to develop a composite functional health prediction model using Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). Tracking functional health objectively could help clinicians to make decisions for interventions in case of functional health deterioration. We next constructed models for fall risk prediction in older adults using geriatric assessments, demographic data, and GAITRite assessment data. A 6-month fall risk prediction model was developed with data from 93 older adult residents. Explainable AI techniques were used to provide explanations to the model predictions, such as which specific features increased the risk of fall in a particular model prediction. Such explanations to model predictions provide valuable insights for targeted interventions. In another study, we developed deep neural network models to predict fall risk from de-identified nursing notes data from 162 older adult residents from TigerPlace. Clinical nursing notes have been shown to contain valuable information related to fall risk factors. This analysis provides the groundwork for future experiments to predict fall risk in older adults using clinical notes. In addition to using EHR data to predict functional health and fall risk in older adults, two studies were conducted to predict fall and functional health from in-home sensor data. Models for in-home fall prediction using depth sensor imagery have been successfully used at TigerPlace. However, the model is prone to false fall alarms in several scenarios, such as pillows thrown on the floor and pets jumping from couches. A secondary fall analysis was performed by analyzing fall alert videos to further identify and remove false alarms. In the final study, we used in-home sensor data streaming from depth sensors and bed sensors to predict functional health and absolute geriatric assessment values. These prediction models can be used to predict the functional health of residents in absence of sparse and infrequent geriatric assessments. This can also provide continuous tracking of functional health in older adults using the streaming in-home sensor data

Participative Urban Health and Healthy Aging in the Age of AI

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems

The Effect of Locomotor Assisted Therapy on Lower Extremity Motor Performance in Typically Developing Children and Children with Cerebral Palsy

Indiana University-Purdue University Indianapolis (IUPUI)Background: Ambulation is critical to a child’s participation, development of selfconcept, and quality of life. Children with cerebral palsy (CP) frequently exhibit limitation in walking proficiency which has been identified as the primary physical disability. Traditional rehabilitative treatment techniques to improve ambulation for children with CP reveal inconsistent results. Driven gait orthosis (DGO) training is a novel approach focusing on motor learning principles that foster cortical neural plasticity. Objective: The objectives are to determine if: (i) the lower extremity muscle activation patterns of children with CP are similar to age-matched TD children in overground (OG) walking, (ii) DGO training replicates muscle activation patterns in OG ambulation in TD children, (iii) the lower extremity muscle activation patterns in OG walking of children with CP are similar to their muscle activation patterns with DGO assistance, and (iv) DGO training promotes unimpaired muscle activation patterns in children with CP. Methods: Muscle activity patterns of the rectus femoris, semitendinosus, gluteus maximus and gluteus medius were recorded in the OG and DGO walking conditions of children with CP and age-matched TD. The gait cycles were identified and the data was averaged to produce final average gait cycle time normalized values. Results: In comparing the variability of the muscle activation patterns within the subject groups, CP DGO walking was considerably lower than CP OG. In comparing the muscle activation patterns in each condition, consistent differences (p < .05) were noted in terminal stance, pre-swing and initial swing phases of gait with the DGO condition consistently revealing greater muscle unit recruitment. Conclusion: The results indicate that training in the DGO provided the ability to practice with measurably repetitive movement as evidenced by decreased variability. Consistent differences were noted in muscle activation patterns in the terminal stance, pre-swing and initial swing phases of gait when most of these muscles are primarily inactive. The alteration in ground reaction force within the DGO environment may play a role in this variance. With the goal of normalizing gait, it is important that the effect of these parameters on ground reaction forces be considered in the use of DGO rehabilitation

Review of automated systems for upper limbs functional assessment in neurorehabilitation

Traditionally, the assessment of upper limb (UL) motor function in neurorehabilitation is carried out by clinicians using standard clinical tests for objective evaluation, but which could be influenced by the clinician's subjectivity or expertise. The automation of such traditional outcome measures (tests) is an interesting and emerging field in neurorehabilitation. In this paper, a systematic review of systems focused on automation of traditional tests for assessment of UL motor function used in neurological rehabilitation is presented. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the automation level, the data acquisition systems, the outcome generation method, and the focus of assessment. Finally, a series of technical requirements, guidelines, and challenges that must be considered when designing and implementing fully-automated systems for upper extremity functional assessment are summarized. This paper advocates the use of automated assessment systems (AAS) to build a rehabilitation framework that is more autonomous and objective.This work was supported in part by the Spanish Ministry of Economy and Competitiveness via the ROBOHEALTH (DPI2013-47944-C4-1-R) and ROBOESPAS (DPI2017-87562-C2-1-R) Projects, and in part by the RoboCity2030-III-CM project (S2013/MIT-2748) which is funded by the Programas de Actividades I+D Comunidad de Madrid and cofunded by the Structural Funds of the EU